AIMC Topic: Mathematics

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A machine-learning model of academic resilience in the times of the COVID-19 pandemic: Evidence drawn from 79 countries/economies in the PISA 2022 mathematics study.

The British journal of educational psychology
BACKGROUND: Given that students from socio-economically disadvantaged family backgrounds are more likely to suffer from low academic performance, there is an interest in identifying features of academic resilience, which may mitigate the relationship...

Advanced technologies and mathematical metacognition: The present and future orientation.

Bio Systems
The intersection of mathematical cognition, metacognition, and advanced technologies presents a frontier with profound implications for human learning and artificial intelligence. This paper traces the historical roots of these concepts from the Pyth...

Solving olympiad geometry without human demonstrations.

Nature
Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning approaches...

Geometric Deep Learning sub-network extraction for Maximum Clique Enumeration.

PloS one
The paper presents an algorithm to approach the problem of Maximum Clique Enumeration, a well known NP-hard problem that have several real world applications. The proposed solution, called LGP-MCE, exploits Geometric Deep Learning, a Machine Learning...

Incorrect Application of Yilmaz-Poli (2022) Initialisation Method in dePater-Mitici 2023 paper entitled "A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers".

Neural networks : the official journal of the International Neural Network Society
In this letter to the editor we report on a methodological error made in the article entitled "A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers" by dePater and Mitici recently appeared in this ...

Unveiling the benefits of multitasking in disentangled representation formation.

Trends in cognitive sciences
Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneous tasks. Such experiments explore the benefits of flexible representations and add to a growing fie...

Mathematical Geometry and Groups for Low-Symmetry Metal Complex Systems.

Molecules (Basel, Switzerland)
Since chemistry, materials science, and crystallography deal with three-dimensional structures, they use mathematics such as geometry and symmetry. In recent years, the application of topology and mathematics to material design has yielded remarkable...

Artificial neural network modelling of the neural population code underlying mathematical operations.

NeuroImage
Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent ne...

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective.

IEEE/ACM transactions on computational biology and bioinformatics
The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning...

Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning.

Sensors (Basel, Switzerland)
Advances in neural networks have garnered growing interest in applications of machine vision in livestock management, but simpler landmark-based approaches suitable for small, early stage exploratory studies still represent a critical stepping stone ...